Related papers: Constraints on prospective deviations from the col…
The local measurement of $H_0$ is in tension with the prediction of $\Lambda$CDM model based on the Planck data. This tension may imply that dark energy is strengthened in the late-time Universe. We employ the latest cosmological…
While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from {\em small} data. In…
We consider a new cosmological model (called $\tilde\Lambda$CDM) in which the vacuum energy interacts with matter and radiation, and test this model using the current cosmological observations. Using the CMB+BAO+SN (CBS) dataset to…
In this study, we employ a two-step method to analyze models of holographic dark energy (HDE) and interacting holographic dark energy (IHDE), incorporating three distinct dark energy (DE)-dark matter (DM) interaction terms. First, using the…
Currently, the standard cosmological model faces some tensions and discrepancies between observations at early and late cosmological time. One of them concerns the well-known $H_0$-tension problem, i.e., a $\sim4.4\sigma$-difference between…
The Gaussian process (GP) regression model is a widely employed surrogate modeling technique for computer experiments, offering precise predictions and statistical inference for the computer simulators that generate experimental data.…
Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography -- mapping the time evolution of the cosmic expansion -- in a model-independent…
We examine the dark energy and matter densities allowed by precision measurements of distances out to various redshifts, in the presence of spatial curvature and (near) arbitrary behavior of the dark energy equation of state. Degeneracies…
Cosmology enters precision testing period with the observational experiments advancement. We have not arrived to decipher the origin of the dominant dark energy component of our universe, although we have located some of its characters. In…
We present a novel computational approach for extracting weak signals, whose exact location and width may be unknown, from complex background distributions with an arbitrary functional form. We focus on datasets that can be naturally…
We develop a generative model for the nuclear matter equation of state at zero net baryon density using the Gaussian Process Regression method. We impose first-principles theoretical constraints from lattice QCD and hadron resonance gas at…
The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the stochastic alternating current (AC) chance-constrained (CC) Optimal Power Flow (OPF) problem. Although the AC CC-OPF problem has been…
The current accelerated expansion of the Universe remains ones of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced…
We investigate the Tsallis holographic dark energy (THDE) models in the context of perturbations' growth. We assume the description of dark energy by considering the holographic principle and the nonadditive entropy to carry out this. We…
Diverse domains of science and engineering use parameterised mechanistic models. Engineers and scientists can often hypothesise several rival models to explain a specific process or phenomenon. Consider a model discrimination setting where…
In this article we use the latest cosmological observations, including SNe, BAO, CC and RSD, to reconstruct the cosmological evolution via the Gaussian process. At the background level, we find consistency with the quintom dynamics for…
We show that relaxion, that addresses the hierarchy problem, can account for the observed dark matter (DM) relic density. The setup is similar to the case of axion DM models topped with a dynamical misalignment mechanism. After the…
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify…
We investigate the cosmological applications of new gravitational scalar-tensor theories and we analyze them in the light of $H_0$ tension. In these theories the Lagrangian contains the Ricci scalar and its first and second derivatives in a…
In this work the results from the quantum process of matter creation have been used in order to constrain the mass of the dark matter particles in an accelerated Cold Dark Matter model (Creation Cold Dark Matter, CCDM). In order to take…